Input Stock Data
Paste your historical stock data below or upload a file. The data must be tab-separated.
Requires at least 10 data points (26+ recommended for all indicators). The column order must be: Timestamp High Low Open Close Volume.
Tip: You can copy this data from financial websites like Yahoo Finance or MSN Money. Just navigate to the "Historical Data" tab for a stock and copy the table.
Price Chart (Last 30 Periods + HMM Prediction)
AI-Powered News Sentiment Analysis
Paste a news article or financial report below to get an AI-generated analysis of its potential impact on a stock.
Analysis Panel Guide: When to Rely on Which Model
- HMM (Hidden Markov Model): Best for Regime Detection. It identifies if the market is in a "hidden mood" like extreme volatility or a steady trend. Rely on this when you feel the market "character" is changing.
- Reservoir Computing (RC): Best for Temporal Momentum. It captures the memory of recent price "ripples". Rely on this for short-term direction when the market is trending smoothly.
- Technical Analysis (RSI, MACD, BB): Best for Standard Setups. These are mathematical rules used by millions of traders. Rely on these to confirm if a price is statistically "too high" (RSI) or if a trend is crossing over (MACD).
The Analysis Engines
This tool uses a suite of client-side analysis techniques. The entire calculation happens in your browser—no data is sent to a server. The primary model is a Hidden Markov Model (HMM), supplemented by standard technical indicators.
HMM In simple terms: Think of the market as having hidden "moods" (like 'optimistic', 'cautious', or 'panicked'). We can't see these moods directly, but we can see their effects: the daily price movements (e.g., 'big jump up', 'small dip down'). The HMM learns two key things from the historical data: 1) How likely the market is to switch from one mood to another, and 2) Which price movements are typical for each mood. To make a prediction, it looks at today's price movement, makes its best guess about the market's current mood, and then predicts the next mood and the price action that will likely follow.
- Vector Quantization (HMM): The engine converts each data point into a vector based on its price changes. It then groups these vectors into a smaller set of distinct "market patterns" using k-means clustering.
- Model Training (HMM): Using the Baum-Welch algorithm, the HMM learns the probabilities of transitioning between different unobservable "hidden market states" (e.g., 'bullish', 'bearish') and which visible market patterns each state produces.
- Prediction (HMM): Based on the most recent data, the engine calculates the most probable state for the next period and predicts the resulting price movement, leading to a Buy, Sell, or Hold signal.
- Technical Indicators: Standard indicators like ARIMA, Bollinger Bands, MACD, and RSI are also calculated. They analyze aspects like momentum, trend, and volatility to provide additional, independent signals.
Important Information
For Informational Purposes Only
This tool is for educational purposes only. The predictions are based on a mathematical model applied to past data and are not guaranteed to be accurate. This is not financial advice. Always consult a qualified financial professional before making investment decisions.